diff --git a/R/get_census_api_2.R b/R/get_census_api_2.R
index 390bdde..1920658 100644
--- a/R/get_census_api_2.R
+++ b/R/get_census_api_2.R
@@ -22,8 +22,8 @@
 #'  If unsuccessful, function prints the URL query that was constructed.
 #'
 #' @examples
-#' \dontrun{get_census_api_2(data_url = "https://api.census.gov/data/2010/dec/sf1?", key = "...", 
-#' get = c("P005003","P005004","P005005", "P005006"), region = "for=county:*&in=state:34")}
+#' \dontrun{try(get_census_api_2(data_url = "https://api.census.gov/data/2010/dec/sf1?", key = "...", 
+#' get = c("P005003","P005004","P005005", "P005006"), region = "for=county:*&in=state:34"))}
 #' 
 #' @references
 #' Based on code authored by Nicholas Nagle, which is available 
diff --git a/R/merge_names.R b/R/merge_names.R
index c54d705..4fe4cbe 100644
--- a/R/merge_names.R
+++ b/R/merge_names.R
@@ -64,7 +64,7 @@
 #' @importFrom dplyr coalesce
 #' @examples
 #' data(voters)
-#' \dontrun{merge_names(voters, namesToUse = "surname", census.surname = TRUE)}
+#' \dontrun{try(merge_names(voters, namesToUse = "surname", census.surname = TRUE))}
 #' @keywords internal
 merge_names <- function(voter.file, namesToUse, census.surname, table.surnames = NULL, table.first = NULL, table.middle = NULL, clean.names = TRUE, impute.missing = FALSE, model = "BISG") {
 
diff --git a/R/merge_surnames.R b/R/merge_surnames.R
index 45c85cb..f7c6cd4 100644
--- a/R/merge_surnames.R
+++ b/R/merge_surnames.R
@@ -50,7 +50,7 @@
 #'#'
 #' @examples
 #' data(voters)
-#' \dontrun{merge_surnames(voters)}
+#' \dontrun{try(merge_surnames(voters))}
 #'
 #' @keywords internal
 
diff --git a/R/predict_race.R b/R/predict_race.R
index 85263e9..aea58f6 100644
--- a/R/predict_race.R
+++ b/R/predict_race.R
@@ -107,24 +107,28 @@
 #'
 #' @examples
 #' data(voters)
-#' predict_race(voter.file = voters, surname.only = TRUE)
+#' try(predict_race(voter.file = voters, surname.only = TRUE))
 #' \dontrun{
-#' predict_race(voter.file = voters, census.geo = "tract", census.key = "...")
+#' try(predict_race(voter.file = voters, census.geo = "tract", census.key = "..."))
 #' }
 #' \dontrun{
-#' predict_race(voter.file = voters, census.geo = "place", census.key = "...", year = "2020")
+#' try(predict_race(
+#'   voter.file = voters, census.geo = "place", census.key = "...", year = "2020"))
 #' }
 #' \dontrun{
-#' CensusObj <- get_census_data("...", state = c("NY", "DC", "NJ"))
-#' predict_race(voter.file = voters, census.geo = "tract", census.data = CensusObj, party = "PID")
+#' CensusObj <- try(get_census_data("...", state = c("NY", "DC", "NJ")))
+#' try(predict_race(
+#'   voter.file = voters, census.geo = "tract", census.data = CensusObj, party = "PID")
+#'   )
 #' }
 #' \dontrun{
-#' CensusObj2 <- get_census_data(key = "...", state = c("NY", "DC", "NJ"), age = T, sex = T)
-#' predict_race(voter.file = voters, census.geo = "tract", census.data = CensusObj2, age = T, sex = T)
+#' CensusObj2 <- try(get_census_data(key = "...", state = c("NY", "DC", "NJ"), age = T, sex = T))
+#' try(predict_race(
+#'   voter.file = voters, census.geo = "tract", census.data = CensusObj2, age = T, sex = T))
 #' }
 #' \dontrun{
-#' CensusObj3 <- get_census_data(key = "...", state = c("NY", "DC", "NJ"), census.geo = "place")
-#' predict_race(voter.file = voters, census.geo = "place", census.data = CensusObj3)
+#' CensusObj3 <- try(get_census_data(key = "...", state = c("NY", "DC", "NJ"), census.geo = "place"))
+#' try(predict_race(voter.file = voters, census.geo = "place", census.data = CensusObj3))
 #' }
 #' @export
 
diff --git a/README.md b/README.md
index 1fbabd1..f26ff01 100644
--- a/README.md
+++ b/README.md
@@ -105,7 +105,7 @@ predict_race(voter.file = voters.dc.nj, census.geo = "county", census.data = cen
 predict_race(voter.file = voters.dc.nj, census.geo = "tract", census.data = census.dc.nj2, party = "PID", age = TRUE, sex = FALSE)  # Pr(Race | Surname, Tract, Party)
 ```
 
-Or you can also use the census_geo_api() to maually construct a census object. The example below creates a census object with county-level and tract-level data in DC and NJ, while avoiding downloading block-level data. Note that this function has the input parameter 'state' that requires a two-letter state abbreviation to proceed.
+Or you can also use the census_geo_api() to manually construct a census object. The example below creates a census object with county-level and tract-level data in DC and NJ, while avoiding downloading block-level data. Note that this function has the input parameter 'state' that requires a two-letter state abbreviation to proceed.
 
 ``` r
 censusObj2  = list()
diff --git a/man/format_legacy_data.Rd b/man/format_legacy_data.Rd
index 5958c9f..eefc5f3 100644
--- a/man/format_legacy_data.Rd
+++ b/man/format_legacy_data.Rd
@@ -29,7 +29,7 @@ available for the 2020 Census as of September 2021.
 }
 \examples{
 \dontrun{
-gaCensusData <- format_legacy_data('ga2020.pl')
+gaCensusData <- format_legacy_data(PL94171::pl_url('GA', 2020))
 predict_race_new(ga.voter.file, namesToUse = 'last, first, mid', census.geo = 'block',
      census.data = gaCensusData)
 }
diff --git a/man/get_census_api_2.Rd b/man/get_census_api_2.Rd
index ec604a5..4b43f27 100644
--- a/man/get_census_api_2.Rd
+++ b/man/get_census_api_2.Rd
@@ -37,8 +37,8 @@ It is used by the \code{get_census_api} function. The user should not need to ca
 function directly.
 }
 \examples{
-\dontrun{get_census_api_2(data_url = "https://api.census.gov/data/2010/dec/sf1?", key = "...", 
-get = c("P005003","P005004","P005005", "P005006"), region = "for=county:*&in=state:34")}
+\dontrun{try(get_census_api_2(data_url = "https://api.census.gov/data/2010/dec/sf1?", key = "...", 
+get = c("P005003","P005004","P005005", "P005006"), region = "for=county:*&in=state:34"))}
 
 }
 \references{
diff --git a/man/merge_names.Rd b/man/merge_names.Rd
index 4fcaa3b..114c6fe 100644
--- a/man/merge_names.Rd
+++ b/man/merge_names.Rd
@@ -93,6 +93,6 @@ This function allows users to match names in their dataset with database entries
 }
 \examples{
 data(voters)
-\dontrun{merge_names(voters, namesToUse = "surname", census.surname = TRUE)}
+\dontrun{try(merge_names(voters, namesToUse = "surname", census.surname = TRUE))}
 }
 \keyword{internal}
diff --git a/man/merge_surnames.Rd b/man/merge_surnames.Rd
index e3490a5..ba4a071 100644
--- a/man/merge_surnames.Rd
+++ b/man/merge_surnames.Rd
@@ -72,7 +72,7 @@ This function allows users to match surnames in their dataset with the U.S.
 }
 \examples{
 data(voters)
-\dontrun{merge_surnames(voters)}
+\dontrun{try(merge_surnames(voters))}
 
 }
 \keyword{internal}
diff --git a/man/predict_race.Rd b/man/predict_race.Rd
index 7649a7a..8900357 100644
--- a/man/predict_race.Rd
+++ b/man/predict_race.Rd
@@ -156,23 +156,23 @@ individual-level race/ethnicity, based on surname, geolocation, and party.
 }
 \examples{
 data(voters)
-predict_race(voter.file = voters, surname.only = TRUE)
+try(predict_race(voter.file = voters, surname.only = TRUE))
 \dontrun{
-predict_race(voter.file = voters, census.geo = "tract", census.key = "...")
+try(predict_race(voter.file = voters, census.geo = "tract", census.key = "..."))
 }
 \dontrun{
-predict_race(voter.file = voters, census.geo = "place", census.key = "...", year = "2020")
+try(predict_race(voter.file = voters, census.geo = "place", census.key = "...", year = "2020"))
 }
 \dontrun{
-CensusObj <- get_census_data("...", state = c("NY", "DC", "NJ"))
-predict_race(voter.file = voters, census.geo = "tract", census.data = CensusObj, party = "PID")
+CensusObj <- try(get_census_data("...", state = c("NY", "DC", "NJ")))
+try(predict_race(voter.file = voters, census.geo = "tract", census.data = CensusObj, party = "PID"))
 }
 \dontrun{
-CensusObj2 <- get_census_data(key = "...", state = c("NY", "DC", "NJ"), age = T, sex = T)
-predict_race(voter.file = voters, census.geo = "tract", census.data = CensusObj2, age = T, sex = T)
+CensusObj2 <- try(get_census_data(key = "...", state = c("NY", "DC", "NJ"), age = T, sex = T))
+try(predict_race(voter.file = voters, census.geo = "tract", census.data = CensusObj2, age = T, sex = T))
 }
 \dontrun{
-CensusObj3 <- get_census_data(key = "...", state = c("NY", "DC", "NJ"), census.geo = "place")
-predict_race(voter.file = voters, census.geo = "place", census.data = CensusObj3)
+CensusObj3 <- try(get_census_data(key = "...", state = c("NY", "DC", "NJ"), census.geo = "place"))
+try(predict_race(voter.file = voters, census.geo = "place", census.data = CensusObj3))
 }
 }